Hoe kunnen we de Afrikaanse maniok boer het best helpen?
Al jaren breken wetenschappers, politiekers, en ondernemers hun hoofd over hoe we Afrikaanse boeren kunnen helpen meer te produceren. Dit zou namelijk ten goede komen voor het hele continent, en zelfs voor de hele wereld. Eén van de methodes die ingezet werden, zijn zogenaamde "extension services", of voorlichtingsdiensten. Hierbij worden boeren op de hoogte gesteld van nieuwe technologieën die hun oogst doen stijgen. Men dacht dus dat het grootste probleem gewoon een tekort aan informatie was. We verwachtten grote successen, maar helaas bleven die uit. Tijdens het onderzoek naar de oorzaak van de uitgebleven positieve resultaten, besefte men dat de meeste boeren de overgebrachte informatie gewoon negeerde. Het begon te dagen dat een "one-size-fits-all" methode gedoemd was om te mislukken. Je kan namelijk niet hetzelfde advies geven aan alle boeren die zo veel verschillen in hun financiële, geografische, of sociale situatie. Men begreep dat het advies aangepast moest worden aan elke boer zijn/haar persoonlijke situatie.
De African Cassava Agronomy Initiative, of kortweg ACAI, heeft onderzocht welke nieuwe technieken en technologieën de oogst van maniok (in het Engels: cassava) kan verhogen. Aangezien maniok één van de belangrijkste gewassen is in Afrika, kunnen hun bevindingen de Afrikaanse boeren enorm helpen. Maar ACAI besefte dus dat ze hun advies telkens een beetje moesten aanpassen aan elke boer voor optimale toepassing van hun bevindingen. Deze scriptie probeert een methode te vinden voor gemakkelijkere personalisatie van advies, met behulp van keuze-experimenten. Keuze-experimenten helpen de voorkeuren van boeren omtrent de karakteristieken van de te verspreiden technologieën in te schatten. In het geval van ACAI beseften we dat de voorkeur van de boer omtrent zes karakteristieken een invloed kon hebben op hun implementatie. In totaal hebben we 333 boeren geïnterviewd in Tanzania. Dan hebben we onderzocht of er een relatie te vinden was tussen de boeren hun voorkeur en hun socio-economische situatie. Het doel is dus om te weten of en hoe advies aangepast moet worden aan boeren met een verschillende socio-economische achtergrond om de implementatie te verhogen.
De resultaten van de analyse tonen dat advies om verspreid te planten en te oogsten geen personalisatie hoeft, maar een grotere implementatie wordt wel bij de welvarendere boeren verwacht. Voor kunstmestgebruik ligt de grootste aversie bij de boeren met het kleinste maniok veld en de minste ervaring met kunstmestgebruik voor maniok. De minst welvarende boeren tonen aan open te staan voor kunstmestgebruik. Er wordt echter voorspeld dat de implementatie bij hun ook laag zal zijn, tenzij ze toegang hebben tot krediet. Gezien dat ook een gegarandeerde afzetmarkt het meest in smaak valt bij deze minst welvarende klasse, worden beleidsvormers en grote cassave kopers aangeraden om de mogelijkheid van contracten gecombineerd met krediettoegang te analyseren. Zo'n contracten tussen boeren en kopers zou de minst welvarende families een veel betere financiële situatie geven.
Bibliografie
ACAI. (2015). African Cassava Agronomy Initiative (ACAI). Retrieved April 24, 2019, from https://www.iita.org/iita-project/acai-african-cassava-agronomy-initiat…
Adam, C., Bevan, D., & Gollin, D. (2018). Rural–Urban Linkages, Public Investment and Transport Costs: The Case of Tanzania. World Development, 109, 497–510. https://doi.org/10.1016/J.WORLDDEV.2016.08.013
Arndt, C., Farmer, W., Strzepek, K., & Thurlow, J. (2012). Climate Change, Agriculture and Food Security in Tanzania. Review of Development Economics, 16(3), 378–393. https://doi.org/10.1111/j.1467-9361.2012.00669.x
Asrat, S., Yesuf, M., Carlsson, F., & Wale, E. (2010). Farmers’ preferences for crop variety traits: Lessons for on-farm conservation and technology adoption. Ecological Economics, 69(12), 2394–2401. https://doi.org/10.1016/J.ECOLECON.2010.07.006
Audibert, M., He, Y., & Mathonnat, J. (2013). Multinomial and Mixed Logit Modeling in the Presence of Heterogeneity: A Two-Period Comparison of Healthcare Provider Choice in Rural China. Retrieved from www.cerdi.org
Awotide, B. A., Alene, A. D., Abdoulaye, T., & Manyong, V. M. (2015). Impact of agricultural technology adoption on asset ownership: the case of improved cassava varieties in Nigeria. Food Security, 7(6), 1239–1258. https://doi.org/10.1007/s12571- 015-0500-7
Ayenew, H. Y. (2016). Production Efficiency and Market Orientation in Food Crops in North West Ethiopia: Application of Matching Technique for Impact Assessment. PloS One, 11(7), e0158454. https://doi.org/10.1371/journal.pone.0158454
Bationo, A., Ngaradoum, D., Youl, S., Lompo, F., & Fening, J. O. (Eds.). (2018). Improving the Profitability, Sustainability and Efficiency of Nutrients Through Site Specific Fertilizer Recommendations in West Africa Agro-Ecosystems. https://doi.org/10.1007/978-3-319-58789-9
Bennett, J., & Birol, E. (2010). Choice experiments in developing countries : implementation, challenges and policy implications. Retrieved from https://search.proquest.com/docview/761671750?OpenUrlRefId=info:xri/sid… ountid=17215
Boxall, P. C., & Adamowicz, W. L. (2002). Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach. In Environmental and Resource Economics (Vol. 23). Retrieved from https://link-springer-
com.kuleuven.ezproxy.kuleuven.be/content/pdf/10.1023%2FA%3A1021351721619.pdf Bradley, M., & Daly, A. (1994). Use of the logit scaling approach to test for rank-order and
fatigue effects in stated preference data. Transportation, 21(2), 167–184.
https://doi.org/10.1007/BF01098791
Brownstone, D., & Small, K. A. (2005). Valuing time and reliability: assessing the evidence
from road pricing demonstrations. Transportation Research Part A: Policy and
Practice, 39(4), 279–293. https://doi.org/10.1016/j.tra.2004.11.001
Cai, Y., Golub, A. A., & Hertel, T. W. (2017). Agricultural research spending must increase
in light of future uncertainties. Food Policy, 70, 71–83.
https://doi.org/10.1016/J.FOODPOL.2017.06.002
Carter, M. R. (2016). What farmers want: the “gustibus multiplier” and other behavioral
insights on agricultural development. Agricultural Economics, 47(S1), 85–96.
https://doi.org/10.1111/agec.12312
Cascetta, E. (2009). TRANSPORTATION SYSTEMS ANALYSIS. Retrieved from https://link-
springer-com.kuleuven.ezproxy.kuleuven.be/content/pdf/10.1007%2F978-0-387-75857-
2.pdf
Caussade, S., Ortúzar, J. de D., Rizzi, L. I., & Hensher, D. A. (2005). Assessing the influence
of design dimensions on stated choice experiment estimates. Transportation Research
Part B: Methodological, 39(7), 621–640. https://doi.org/10.1016/J.TRB.2004.07.006 CGIAR. (2019). CGIAR: Science for humanity’s greatest challenges. Retrieved March 15,
2019, from https://www.cgiar.org/
ChoiceMetrics. (2018). Ngene 1.2 USER MANUAL & REFERENCE GUIDE The
Cutting Edge in Experimental Design. Retrieved from www.choice-metrics.com Dalemans, F., Muys, B., & Maertens, M. (2019). Adoption Constraints for Small-scale Agroforestry-based Biofuel Systems in India. Ecological Economics, 157, 27–39.
https://doi.org/10.1016/J.ECOLECON.2018.10.020
Dercon, S., & Christiaensen, L. (2011). Consumption risk, technology adoption and poverty
traps: Evidence from Ethiopia. Journal of Development Economics, 96(2), 159–173.
https://doi.org/10.1016/J.JDEVECO.2010.08.003
Dinar, A., Karagiannis, G., & Tzouvelekas, V. (2007). Evaluating the impact of agricultural
extension on farms’ performance in Crete: a nonneutral stochastic frontier approach. Agricultural Economics, 36(2), 135–146. https://doi.org/10.1111/j.1574- 0862.2007.00193.x
Duquette, E., Higgins, N., & Horowitz, J. (2013). Time Preference and Technology X
Adoption: A Single-Choice Experiment with U.S. Farmers. 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. Retrieved from https://ideas.repec.org/p/ags/aaea13/150719.html
Eicher, C. K. (1990). Building African scientific capacity for agricultural development. Agricultural Economics, 4(2), 117–143. https://doi.org/10.1016/0169-5150(90)90028-Y
Ellsberg, D. (1961). Risk, Ambiguity, and the Savage Axioms. The Quarterly Journal of Economics, 75(4), 643. https://doi.org/10.2307/1884324
Emmanuel, D., Owusu-Sekyere, E., Owusu, V., & Jordaan, H. (2016). Impact of agricultural extension service on adoption of chemical fertilizer: Implications for rice productivity and development in Ghana. NJAS - Wageningen Journal of Life Sciences, 79, 41–49. https://doi.org/10.1016/J.NJAS.2016.10.002
Evenson, R. E., & Gollin, D. (2003). Assessing the impact of the green revolution, 1960 to 2000. National Library of Medicine, 300(5620), 758–762. Retrieved from https://search- proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1875859882?rfr_id=info%3Axri%2Fsid% 3Aprimo
FAO, & IFAD. (2005). A review of cassava in Africa with country case studies on Nigeria, Ghana, the United Republic of Tanzania, Uganda and Benin. Retrieved from http://www.fao.org/3/a-a0154e.pdf
FAOSTAT. (2017). FAOSTAT. Retrieved April 22, 2019, from http://www.fao.org/faostat/en/#data/QC
Fermont, A. M., van Asten, P. J. A., Tittonell, P., van Wijk, M. T., & Giller, K. E. (2009). Closing the cassava yield gap: An analysis from smallholder farms in East Africa. Field Crops Research, 112(1), 24–36. https://doi.org/10.1016/J.FCR.2009.01.009
Gangwar, L. S., Saran, S., & Kumar, S. (2013). Impact of Public Sector Research and Extension on Backyard Poultry Production in Kumaon Hills - An Economic Analysis. Retrieved April 18, 2019, from https://search-proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1475042943?rfr_id=info%3Axri%2Fsid% 3Aprimo
Gómez, W., Salgado, H., Vásquez, F., & Chávez, C. (2014). Using stated preference methods to design cost-effective subsidy programs to induce technology adoption: An application to a stove program in southern Chile. Journal of Environmental Management, 132, 346– 357. https://doi.org/10.1016/J.JENVMAN.2013.11.020
Haji, J. (2007). Production Efficiency of Smallholders’ Vegetable-dominated Mixed Farming XI
System in Eastern Ethiopia: A Non-Parametric Approach. Journal of African
Economies, 16(1), 1–27. https://doi.org/10.1093/jae/ejl044
Hillocks, R. J. (2014). Addressing the Yield Gap in Sub-Saharan Africa. Outlook on
Agriculture, 43(2), 85–90. https://doi.org/10.5367/oa.2014.0163
Howeler, R., Lutaladio, N., & Thomas, G. (2013). Save and grow: Cassava. Retrieved from
http://www.fao.org/3/a-i3278e.pdf
Hu, & Wuyang. (2006). Effects of Endogenous Task Complexity and the Endowed Bundle
on Stated Choice. 2006 Annual Meeting, July 23-26, Long Beach, CA. Retrieved from
https://ideas.repec.org/p/ags/aaea06/21437.html
IITA. (2014). Grant Proposal Narrative ACAI.
Jarvis, A., Ramirez-Villegas, J., Herrera Campo, B. V., & Navarro-Racines, C. (2012). Is
Cassava the Answer to African Climate Change Adaptation?
https://doi.org/10.1007/s12042-012-9096-7
Kalirajan, K. P., & Shand, R. T. (1988). Firm and product-specific technical efficiencies in a
multiproduct cycle system. The Journal of Development Studies, 25(1), 83–96.
https://doi.org/10.1080/00220388808422096
Kassahun, H. T., & Jacobsen, J. B. (2015). Economic and institutional incentives for
managing the Ethiopian highlands of the Upper Blue Nile Basin: A latent class analysis.
Land Use Policy, 44, 76–89. https://doi.org/10.1016/j.landusepol.2014.11.017
Khan, M. H., & Akbari, A. H. (1986). Impact of Agricultural Research and Extension on crop
productivity in Pakistan: A production function approach. World Development, 14(6),
757–762. https://doi.org/10.1016/0305-750X(86)90017-3
Khandker, S., & Mahmud, W. (2012). Seasonal Hunger and Public Policies: Evidence from
Northwest Bangladesh - Shahidur R. Khandker, Wahiduddin Mahmud - Google Books. Retrieved from https://books.google.be/books?id=8MK3ka6UuhkC&pg=PA17&lpg=PA17&dq=hunger +crop+cassava&source=bl&ots=lC8cVKczAG&sig=ACfU3U2h1P2Lza8WCsCH8Ule_ - 5ztRYqXw&hl=en&sa=X&ved=2ahUKEwjq05e2y4ThAhXlXhUIHUY9ChEQ6AEwF XoECAkQAQ#v=onepage&q=famine reserve&f=false
Kidanemariam, G. (2017). South African journal of economic and management sciences. In South African Journal of Economic and Management Sciences (Vol. 20). Retrieved from https://sajems.org/index.php/sajems/article/view/1349/849
Krishnan, P., & Patnam, M. (2014). Neighbors and Extension Agents in Ethiopia: Who XII
Matters More for Technology Adoption? American Journal of Agricultural Economics,
96(1), 308–327. https://doi.org/10.1093/ajae/aat017
Lambrecht, I., Vanlauwe, B., & Maertens, M. (2016a). Agricultural extension in Eastern
Democratic Republic of Congo: does gender matter? European Review of Agricultural
Economics, 43(5), 841–874. https://doi.org/10.1093/erae/jbv039
Lambrecht, I., Vanlauwe, B., & Maertens, M. (2016b). Integrated soil fertility management:
from concept to practice in Eastern DR Congo. International Journal of Agricultural
Sustainability, 14(1), 100–118. https://doi.org/10.1080/14735903.2015.1026047 Lambrecht, I., Vanlauwe, B., Merckx, R., & Maertens, M. (2014). Understanding the Process
of Agricultural Technology Adoption: Mineral Fertilizer in Eastern DR Congo. World
Development, 59, 132–146. https://doi.org/10.1016/J.WORLDDEV.2014.01.024 Lambrecht, I., Vranken, L., Merckx, R., Vanlauwe, B., & Maertens, M. (2015). Ex Ante
Appraisal of Agricultural Research and Extension. Outlook on Agriculture, 44(1), 61–
67. https://doi.org/10.5367/oa.2015.0199
Liverpool, L. S. O., & Winter-Nelson, A. (2010). Poverty Status and the Impact of Formal
Credit on Technology Use and Wellbeing among Ethiopian Smallholders. World
Development, 38(4), 541–554. https://doi.org/10.1016/J.WORLDDEV.2009.11.006 Maertens, A., & Barrett, C. B. (2012). Measuring Social Networks’ Effects on Agricultural Technology Adoption. American Journal of Agricultural Economics, 95(2), 353–359.
https://doi.org/10.1093/ajae/aas049
Mathijs, E., & Vranken, L. (2010). Post-Communist Economies Human Capital, Gender and
Organisation in Transition Agriculture: Measuring and Explaining the Technical Efficiency of Bulgarian and Hungarian Farms. https://doi.org/10.1080/14631370120052654
McSweeney, C., New, M., & Lizcano, G. (2010). UNDP Climate Change Country Profiles: Tanzania | Climate and Development Learning Platform. Retrieved from https://www.climatelearningplatform.org/undp-climate-change-country-pro…- tanzania
Mooney, D. F., Assistant, R., Student, P., & Barham, B. L. (2013). What Drives the Adoption of Clean Agricultural Technologies? An Ex Ante Assessment of Sustainable Biofuel Production in Southwestern Wisconsin. Retrieved from https://ageconsearch.umn.edu/record/150557/files/AAEA - Clean technology - 09- 19.pdf
Nakano, Y., Kajisa, K., & Otsuka, K. (2015). On the Possibility of Rice Green Revolution in XIII
Irrigated and Rainfed Areas in Tanzania: An Assessment of Management Training and Credit Programs. Retrieved from https://ageconsearch.umn.edu/record/212036/files/Tanzania rice technology1.pdf
Nakano, Y., Tsusaka, T. W., Aida, T., & Pede, V. O. (2018). Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania. World Development, 105, 336–351. https://doi.org/10.1016/J.WORLDDEV.2017.12.013
National Agricultural and Forestry Extension Service (NAFES). (2005). Consolidating Extension in the Lao PDR. Retrieved from http://www.laolink.org/Literature/Consolidating_Extension_Laos.pdf
Ngailo, S., Shimelis, H. A., Sibiya, J., & Mtunda, K. (2016). Assessment of sweetpotato farming systems, production constraints and breeding priorities in eastern Tanzania. South African Journal of Plant and Soil, 33(2), 105–112. https://doi.org/10.1080/02571862.2015.1079933
Oakley, P., & Garforth, C. (1985). Guide to extension training. Retrieved from http://www.fao.org/3/a-t0060e.pdf
Obisesan, A. (2014). Gender Differences In Technology Adoption And Welfare Impact Among Nigerian Farming Households. In IDEAS Working Paper Series from RePEc; St. Louis. Retrieved from https://search-proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1700391295?rfr_id=info%3Axri%2Fsid% 3Aprimo
Obisesan, A. A., Amos, T. T., & Akinlade, R. J. (2016). CAUSAL EFFECT OF CREDIT AND TECHNOLOGY ADOPTION ON FARM OUTPUT AND INCOME: THE CASE OF CASSAVA FARMERS IN SOUTHWEST NIGERIA. Retrieved from https://ageconsearch.umn.edu/record/246443/files/182. Causal effect of credit and technology adoption in Nigeria.pdf
Otsuka, K., & Larson, D. F. (2013). An African Green Revolution: Finding Ways to Boost Productivity on Small Farms. Retrieved from https://link-springer- com.kuleuven.ezproxy.kuleuven.be/content/pdf/10.1007%2F978-94-007-5760-8.pdf
Owens, T., & Hoddinott, J. (2001). The impact of agricultural extension on farm production in resettlement areas of Zimbabwe. Retrieved April 18, 2019, from https://search- proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1698920961?rfr_id=info%3Axri%2Fsid% 3Aprimo
XIV
Parmar, A., Sturm, B., & Hensel, O. (2017). Crops that feed the world: Production and improvement of cassava for food, feed, and industrial uses. https://doi.org/10.1007/s12571-017-0717-8
Sanga, C., Kalungwizi, V., & Msuya, C. (2013). Building an agricultural extension services system supported by ICTs in Tanzania: Progress made, Challenges remain. International Journal of Education and Development Using Information and Communication Technology, 9(1), 80–99. Retrieved from https://search-proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1353085132?rfr_id=info%3Axri%2Fsid% 3Aprimo
Sewando, P. T. (2014). Development in Practice Cassava value chain and its products in Morogoro rural district, Tanzania. https://doi.org/10.1080/09614524.2014.966653 Skreli, E., Imami, D., & Zvyagintsev, D. (2014). Government Extension Service Impact
Assessment. Retrieved from
https://ageconsearch.umn.edu/record/169394/files/paper_Skreli_Imami_Zvy… Stata. (2019). Factor analysis. Retrieved from
https://www.stata.com/manuals13/mvfactor.pdf
Suvedi, M., Ghimire, R., & Kaplowitz, M. (2017). Farmers’ participation in extension
programs and technology adoption in rural Nepal: a logistic regression analysis. The Journal of Agricultural Education and Extension, 23(4), 351–371. https://doi.org/10.1080/1389224X.2017.1323653
Uchechukwu-Agua, A. D., Caleb, O. J., & Linus Opara, U. (2015). Postharvest Handling and Storage of Fresh Cassava Root and Products: a Review. https://doi.org/10.1007/s11947- 015-1478-z
UNESCO UIS. (2015). United Republic of Tanzania. Retrieved April 16, 2019, from http://uis.unesco.org/country/TZ
World Bank. (1999). Agricultural Extension_ The Kenya Experience. Retrieved from http://documents.worldbank.org/curated/en/972111468758711518/pdf/multi0…
Wossen, T., Alene, A., Abdoulaye, T., Feleke, S., Rabbi, I. Y., & Manyong, V. (2018). Poverty Reduction Effects of Agricultural Technology Adoption: The Case of Improved Cassava Varieties in Nigeria. Journal of Agricultural Economics. https://doi.org/10.1111/1477-9552.12296
Young, D., & Deng, H. (1999). The effects of education in early-stage agriculture: some evidence from China. https://doi.org/10.1080/000368499323193
